recsys7.py

# neural net based on Embedding matrices
# model reference -> https://github.com/fastai/fastai/
class EmbeddingModel(nn.Module):
    def __init__(self, n_factors, n_users, n_items, y_range, initialise = 0.01):
        super().__init__()
        self.y_range = y_range
        self.u_weight = nn.Embedding(n_users, n_factors)
        self.i_weight = nn.Embedding(n_items, n_factors)
        self.u_bias = nn.Embedding(n_users, 1)
        self.i_bias = nn.Embedding(n_items, 1)
        
        # initialise the weights of the embeddings
        self.u_weight.weight.data.uniform_(-initialise, initialise)
        self.i_weight.weight.data.uniform_(-initialise, initialise)
        self.u_bias.weight.data.uniform_(-initialise, initialise)
        self.i_bias.weight.data.uniform_(-initialise, initialise)

    def forward(self, users, items):
        # dot multiply the weights for the given user_id and item_id
        dot = self.u_weight(users)* self.i_weight(items)
        
        # sum the result of dot multiplication above and add both the bias terms
        res = dot.sum(1) + self.u_bias(users).squeeze() + self.i_bias(items).squeeze()
        
        # return the output in the given range
        return torch.sigmoid(res) * (self.y_range[1]-self.y_range[0]) + self.y_range[0]